What causes the problem for a data culture
The cost of data errors
Over the past few decades, data has become critical for business growth. The world’s fastest-growing companies rely on data to guide their decisions. Thus, governing correct and reliable data is one of the major priorities to the majority of successful businesses today.
Consider this: the global big data market is evaluated at $138.9 billion in 2020 and projected to reach $229.4 billion by 2025. That is not surprising, as the amount of data produced continues to grow. Hence, the data scientist job continues to be ranked as one of the top best jobs in the USA 5 years in a row since 2016.
And yet, there’s the other side of the coin. Another IBM’s study estimated the cost of corrupted data by $3.1 trillion in the USA alone back in 2016. Today’s number has gone far worse, and nearly everyone has been affected by decisions based on imperfect data.
Last year, the United States of America sent $1,4 billion as a coronavirus relief to dead people. It was one of the cases when corrupted data led to significant losses, but such mistakes are common in all verticals, from everyday life to rocket science.
Another infamous example is when a data mistake cost NASA over $327.6 million. The mistake was made because software systems at NASA and the spacecraft builder vendor were using different measurement units; a mismatch that hadn’t been identified at any stage of the testing.
Why do data mistakes happen?
The expected value driven by data analysts, data engineers, or data scientists is to deliver insights that guide business decisions. However, they spent 50% to 80% of their time hunting for data, finding and correcting errors, cleaning and organizing data.
There is nothing available for data reliability matters similar to Site Reliability Engineering. Without a doubt, any person responsible for data in the company would prefer to know something went wrong with data before their boss knows. Precisely this kind of need was behind creating such products as NewRelic, DataDog, or Pager Duty and similar products that are on guard for site reliability and help secure application reliability and allow software development teams to sleep tight.
What should be done? There is no way to achieve data reliability without adopting a strategy. A well-thought strategy fosters data quality in every single way. It’s near to impossible to achieve data reliability just by applying some tool. Everyone at your organization should understand the importance of using fresh, full, and by all means correct data.
Sure as shooting, we live in a data-driven era; Illogically, the available tools on the market do not simply respond to raised problems. Most probably, things will get worse, and a lot of companies will encounter a data crisis as the overall digital business efforts will continue to accelerate. At the same time, companies will also fail to allocate efforts to sustain data reliability.
Actions taken to achieve data reliability will protect business from incorrect and costly decisions based on untrusted data. Without any doubt, data scientists and data analysts are on the front line against this challenge. They are trying to implement validation rules and cover all possible validation scenarios to trust data. But this is not possible to do conveniently because of the amount of data stored in their data warehouses.
Masthead is a platform that is aimed to watch how healthy data is, whether it corresponds to retrospective data, and makes data engineers focus more on building insights for stakeholders.
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